MLmodel | Precision | Recall | F1-score | ACC | ||||||
N | S | P | N | S | P | N | S | P | ||
AdaBoost | 0.960 | 0.576 | 0.854 | 0.863 | 0.861 | 0.854 | 0.909 | 0.690 | 0.854 | 0.862 |
CatBoost | 0.971 | 0.819 | 0.909 | 0.958 | 0.851 | 0.976 | 0.964 | 0.835 | 0.941 | 0.942 |
Decision Tree | 0.961 | 0.743 | 0.812 | 0.940 | 0.772 | 0.951 | 0.950 | 0.757 | 0.876 | 0.914 |
Gradient Boosting | 0.971 | 0.766 | 0.889 | 0.944 | 0.842 | 0.976 | 0.957 | 0.802 | 0.930 | 0.929 |
KNN | 0.961 | 0.526 | 0.761 | 0.849 | 0.802 | 0.854 | 0.901 | 0.635 | 0.805 | 0.842 |
Random Forest | 0.974 | 0.832 | 0.848 | 0.964 | 0.832 | 0.951 | 0.969 | 0.832 | 0.897 | 0.942 |
SVM | 0.977 | 0.491 | 0.487 | 0.782 | 0.802 | 0.902 | 0.869 | 0.609 | 0.632 | 0.793 |
XGBoost | 0.974 | 0.837 | 0.929 | 0.966 | 0.861 | 0.951 | 0.970 | 0.849 | 0.940 | 0.948 |